KUBS 생활
Academic Activities
| Marketing Science Vol. 57, No. 12, December 2011, pp. 2115–2129 Kay Giesecke Department of Management Science and Engineering, Stanford University, Stanford, California 94305 Baeho Kim Korea University Business School, Anam-dong, Sungbuk-gu, Seoul 136-701, Korea Shilin Zhu Department of Statistics, Stanford University, Stanford, California 94305 http://dx.doi.org/10.1287/mnsc.1110.1411 Abstract Dynamic, intensity-based point process models are widely used to measure and price the correlated default risk in portfolios of credit-sensitive assets such as loans and corporate bonds. Monte Carlo simulation is an important tool for performing computations in these models. This paper develops, analyzes, and evaluates two simulation algorithms for intensity-based point process models. The algorithms extend the conventional thinning scheme to the case where the event intensity is unbounded, a feature common to many standard model formulations. Numerical results illustrate the performance of the algorithms for a familiar top-down model and a novel bottom-up model of correlated default risk. This paper was accepted by Assaf Zeevi, stochastic models and simulation. Keywords simulation ; probability ; stochastic model applications ; financial institutions ; banks |